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hbr_article

Retrieve Harvard Business Review article metadata and full body text from a canonical URL. Flags articles originally published in print magazine.

Instructions

Get Harvard Business Review article content. Returns a public Harvard Business Review article's metadata and full body text from a canonical article URL. from_magazine flags articles originally published in the print magazine.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesCanonical hbr.org article URL

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.17.5

TDQS

A4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses that it returns metadata and full body text, and mentions the from_magazine flag. However, it does not mention any side effects (though likely read-only), authentication requirements, or limitations (e.g., only public articles, potential paywall behavior). The description gives basic behavioral info but lacks depth on error handling or return structure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the core purpose ('Get Harvard Business Review article content'), followed by a concise explanation of what it returns and the from_magazine flag. Every sentence earns its place with no fluff or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given only one parameter, no output schema, and no annotations, the description is mostly complete. It explains what the tool returns (metadata and full body text) and the input requirement. It could be more explicit about the output structure (e.g., fields in metadata) or any constraints (e.g., only public articles), but it is sufficient for an agent to understand and call the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The only parameter 'url' is fully described in the schema as 'Canonical hbr.org article URL,' and the description repeats this without adding new meaning. Since schema coverage is 100%, the baseline of 3 applies. The description does not clarify URL format (e.g., must include https://, trailing slash) or provide examples. It adds no value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get Harvard Business Review article content.' It specifies the resource (HBR article), the action (retrieve metadata and full body text), and the input (canonical article URL). This distinguishes it from sibling tools like hbr_headlines (which list headlines) and hbr_categories/hbr_topic (which provide browsing/listing functions). The mention of 'from_magazine' adds a distinguishing detail about output.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage: if you have a canonical hbr.org URL, use this tool to fetch the article. It does not explicitly contrast with alternatives like search or headline tools, but the input requirement ('from a canonical article URL') makes the context clear. It could be improved by explicitly stating 'use this when you have a specific article URL rather than browsing headlines or categories.'

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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